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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS.

David Gerard1, Matthew Stephens2

  • 1Department of Mathematics and Statistics, American University, Washington, DC 20016, USA.

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|December 27, 2023
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Summary
This summary is machine-generated.

This study introduces RUV*, a unified framework for removing unwanted variation in gene expression data. RUV* generalizes existing methods and enables new approaches like RUVB, showing competitive performance in simulations.

Keywords:
Batch effectRNA-seqcorrelated testgene expressionhidden confoundingnegative controlunobserved confoundingunwanted variation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Unwanted variation and hidden confounding are significant challenges in large-scale gene expression studies.
  • Existing methods like RUV1, RUV2, RUV4, RUVinv, RUVrinv, and RUVfun attempt to address this using control genes.

Purpose of the Study:

  • To introduce a general and unifying framework, RUV*, for removing unwanted variation.
  • To clarify the relationships between existing RUV methods and facilitate the development of new ones.

Main Methods:

  • Developed RUV*, a general framework that unifies and generalizes existing RUV methods.
  • Illustrated RUV* by implementing RUVB, a novel version based on Bayesian factor analysis.
  • Evaluated RUVB's performance using realistic simulations based on real data.

Main Results:

  • RUV* clarifies connections between existing methods, showing RUV2 and RUV4 can be equivalent under certain conditions.
  • RUVB demonstrated competitive power and calibration compared to existing methods in simulations.
  • Consistent calibration across diverse datasets remains a challenge.

Conclusions:

  • The RUV* framework offers a unified approach to handling unwanted variation in gene expression data.
  • RUVB presents a promising new method with competitive performance, though calibration challenges persist.
  • Modularity of RUV* facilitates the integration of advanced matrix imputation techniques.